{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "array = np.array([1, 2, 3, 4, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "(5,)"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array.shape"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'list' object has no attribute 'shape'",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mAttributeError\u001B[0m                            Traceback (most recent call last)",
      "Cell \u001B[1;32mIn [4], line 2\u001B[0m\n\u001B[0;32m      1\u001B[0m tang_list\u001B[38;5;241m=\u001B[39m[\u001B[38;5;241m1\u001B[39m,\u001B[38;5;241m2\u001B[39m,\u001B[38;5;241m3\u001B[39m,\u001B[38;5;241m4\u001B[39m,\u001B[38;5;241m5\u001B[39m]\n\u001B[1;32m----> 2\u001B[0m \u001B[43mtang_list\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mshape\u001B[49m\n",
      "\u001B[1;31mAttributeError\u001B[0m: 'list' object has no attribute 'shape'"
     ]
    }
   ],
   "source": [
    "tang_list = [1, 2, 3, 4, 5]\n",
    "tang_list.shape"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[1, 2, 3],\n       [4, 5, 6]])"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([[1, 2, 3], [4, 5, 6]])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "array([1, 2, 3, 4, 5])"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tang_list = [1, 2, 3, 4, 5]\n",
    "tang_array = np.array(tang_list)\n",
    "tang_array"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "array([1., 2., 3., 4., 5.])"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tang_list = [1, 2, 3, 4, 5.0]\n",
    "tang_array = np.array(tang_list)\n",
    "tang_array"
   ],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
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